【導(dǎo)讀】傳統(tǒng)的磁帶語(yǔ)音錄放系統(tǒng)因其體積大，使用不便，在電子信息處。理的使用中受到許多限制。本文提出的體積小巧，功耗低的數(shù)字化語(yǔ)音存儲(chǔ)。與回放系統(tǒng)將完全可以替代它。能，然后通過(guò)分析比較選擇最佳設(shè)計(jì)方案，并完成整個(gè)系統(tǒng)電路的設(shè)計(jì)。文利用單片機(jī)AT89C52控制ISD4004語(yǔ)音芯片來(lái)實(shí)現(xiàn)語(yǔ)音的錄制和播放。ISD4004語(yǔ)音芯片無(wú)須A/D轉(zhuǎn)換和壓縮就可以直接儲(chǔ)存，沒有轉(zhuǎn)換誤差。本文在簡(jiǎn)單分析ISD4004單片語(yǔ)音芯片工作原理的基礎(chǔ)上，通過(guò)。放．通過(guò)外部設(shè)備的擴(kuò)展，可以提高產(chǎn)品的應(yīng)用領(lǐng)域。

　　

【正文】 ,0x20)。 //Power up delay100ms()。 //上電延時(shí) cmdSend(addrs,0xe0)。 //發(fā)地址值為 addr 的 Setplay 命令 cmdSend(0x0000,0xf0)。 //發(fā) Play 命令 } 河南科技大學(xué)本科畢業(yè)設(shè)計(jì)論文 33 //錄音函數(shù) void rec(unsigned int addrs) { cmdSend(0x0000,0x20)。 //發(fā) POWER UP 命令 。 delay100ms()。 //等待 TPUD(上電延時(shí) )。 cmdSend(0x0000,0x20)。 //發(fā) POWER UP 命令 cmdSend(addrs,0xa0)。 //發(fā)地址值為 00的 SETREC 命令 。 cmdSend(0x0000,0xb0)。 //發(fā) REC 命令。 } //停止當(dāng)前操作函數(shù) void stop(void) { cmdSend(0x0000,0x30)。 } void delay(uchar k) { unsigned char a,b。 for(a = k。a0。a) for(b = 1。b。b++)。 } void main() { unsigned int j = 0。 DDRA = 0xff。 河南科技大學(xué)本科畢業(yè)設(shè)計(jì)論文 34 PORTB = PORTB|(1DD_SS)。 //變高 SS // PORTB amp。= ~BIT(DD_MOSI)。 //這個(gè)沒用 的 ?控制不了 在 SPI 下 ? SPI_MasterInit()。 delay(255)。 rec(0)。 PORTA = 0xff。 //開始錄音 ,燈亮 for(。j100。j++) { delay(255)。 } stop()。 delay(255)。 PORTA = 0x00。 //停止錄音 ,燈滅 play(0)。 } 河南科技大學(xué)本科畢業(yè)設(shè)計(jì)論文 35 英文翻譯 Improved speech recognition method for intelligent robot 1. Overview of speech recognition Speech recognition has received more and more attention recently due to the important theoretical meaning and practical value [5 ]. Up to now, most speech recognition is based on conventional linear system theory, such as Hidden Markov Model (HMM) and Dynamic Time Warping(DTW) . With the deep study of speech recognition, it is found that speech signal is a plex nonlinear process. If the study of speech recognition wants to break through, nonlinear system theory method must be introduced to it. Recently, with the developmentof nonlineasystem theories such as artificial neural works(ANN) , chaos and fractal, it is possible to apply these theories to speech recognition. Therefore, the study of this paper is based on ANN and chaos and fractal theories are introduced to process speech recognition. Speech recognition is divided into two ways that are speaker dependent and speaker independent. Speaker dependent refers to the pronunciation model trained by a single person, the identification rate of the training person?sorders is high, while others’orders is in low identification rate or can’t be recognized. Speaker independent refers to the pronunciation model trained by persons of different age, sex and region, it can identify a group of persons’orders. Generally, speaker independent system ismorewidely used, since the user is not required to conduct the training. So extraction of speaker independent features from the speech signal is the fundamental problem of speaker recognition system. Speech recognition can be viewed as a pattern recognition task, which includes training and , speech signal can be viewed as a time sequence and characterized by the powerful hidden Markov model (HMM). Through the feature extraction, the speech signal is transferred into feature vectors and act asobservations. In the training procedure, these observationswill feed to estimate the model parameters of 河南科技大學(xué)本科畢業(yè)設(shè)計(jì)論文 36 HMM. These parameters include probability density function for the observations and their corresponding states, transition probability between the states, etc. After the parameter estimation, the trained models can be used for recognition task. The input observations will be recognized as the resulted words and the accuracy can be evaluated. Thewhole process is illustrated in Fig. 1. Fig. 1 Block diagram of speech recognition system 2. Theory andmethod Extraction of speaker independent features from the speech signa l is the fundamental problem of speaker recognition system. The standard methodology for solving this problem uses Linear Predictive Cepstral Coefficients (LPCC) and MelFrequency Cepstral Coefficient (MFCC). Both these methods are linear procedures based on the assumption that speaker features have properties caused by the vocal tract resonances. These features form the basic spectral structure of the speech signal. However, the non linear information in speech signals is not easily extracted by the present feature extraction methodologies. So we use fractal dimension to measure non2linear speech turbulence. This paper investigates and implements speaker identification system using both traditional LPCC and nonlinear multiscaled fractal dimension feature extraction. 3. L inear Predictive Cepstral Coefficients Linear prediction coefficient (LPC) is a parameter setwhich is obtained when we do linear prediction analysis of speech. It is about some correlation characteristics between adjacent speech samples. Linear prediction analysis is based on the following basic concepts. That is, a speech sample can be estimated approximately by the linear bination of some past speech samples. According to the minimal square sum principle of difference between real speech sample in certain analysis frame shorttime and predictive 河南科技大學(xué)本科畢業(yè)設(shè)計(jì)論文 37 sample, the only group ofprediction coefficients can be determined. LPC coefficient can be used to estimate speech signal cepstrum. This is a special processing method in analysis of speech signal shorttime cepstrum. System function of channelmodel is obtained by linear prediction analysis as follow. Where p represents linear prediction order, ak,(k=1,2,…,p) represent sprediction coefficient, Impulse response is represented by h(n). Suppose cepstrum of h(n) is represented by ,then (1) can be expanded as (2). The cepstrum coefficient calculated in the way of (5) is called LPCC, n represents LPCC order. When we extract LPCC parameter before, we should carry on speech signal preemphasis, framing processing, windowingprocessing and endpoints detection etc. , so 河南科技大學(xué)本科畢業(yè)設(shè)計(jì)論文 38 the endpoint detection of Chinese mand word“Forward”is shown in , next